{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "#导入必要的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>开盘</th>\n",
       "      <th>最高</th>\n",
       "      <th>最低</th>\n",
       "      <th>收盘</th>\n",
       "      <th>涨幅</th>\n",
       "      <th>振幅</th>\n",
       "      <th>总手</th>\n",
       "      <th>金额</th>\n",
       "      <th>换手%</th>\n",
       "      <th>成交次数</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6920</th>\n",
       "      <td>1630.46</td>\n",
       "      <td>1665.46</td>\n",
       "      <td>1614.20</td>\n",
       "      <td>1656.71</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.0321</td>\n",
       "      <td>118020026</td>\n",
       "      <td>1472909400</td>\n",
       "      <td>0.608</td>\n",
       "      <td>67052</td>\n",
       "      <td>1689.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6921</th>\n",
       "      <td>1690.47</td>\n",
       "      <td>1714.22</td>\n",
       "      <td>1667.96</td>\n",
       "      <td>1689.22</td>\n",
       "      <td>0.0196</td>\n",
       "      <td>0.0279</td>\n",
       "      <td>113695774</td>\n",
       "      <td>1467535000</td>\n",
       "      <td>0.586</td>\n",
       "      <td>60751</td>\n",
       "      <td>1712.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6922</th>\n",
       "      <td>1677.96</td>\n",
       "      <td>1747.98</td>\n",
       "      <td>1670.46</td>\n",
       "      <td>1712.97</td>\n",
       "      <td>0.0141</td>\n",
       "      <td>0.0459</td>\n",
       "      <td>107519243</td>\n",
       "      <td>1408651100</td>\n",
       "      <td>0.554</td>\n",
       "      <td>59522</td>\n",
       "      <td>1724.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6923</th>\n",
       "      <td>1736.72</td>\n",
       "      <td>1752.98</td>\n",
       "      <td>1715.47</td>\n",
       "      <td>1724.22</td>\n",
       "      <td>0.0066</td>\n",
       "      <td>0.0219</td>\n",
       "      <td>65301888</td>\n",
       "      <td>861618660</td>\n",
       "      <td>0.337</td>\n",
       "      <td>38464</td>\n",
       "      <td>1697.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6924</th>\n",
       "      <td>1686.72</td>\n",
       "      <td>1710.47</td>\n",
       "      <td>1675.46</td>\n",
       "      <td>1697.97</td>\n",
       "      <td>-0.0152</td>\n",
       "      <td>0.0203</td>\n",
       "      <td>66173879</td>\n",
       "      <td>852956240</td>\n",
       "      <td>0.341</td>\n",
       "      <td>37324</td>\n",
       "      <td>1680.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           开盘       最高       最低       收盘      涨幅      振幅         总手  \\\n",
       "6920  1630.46  1665.46  1614.20  1656.71   0.036  0.0321  118020026   \n",
       "6921  1690.47  1714.22  1667.96  1689.22  0.0196  0.0279  113695774   \n",
       "6922  1677.96  1747.98  1670.46  1712.97  0.0141  0.0459  107519243   \n",
       "6923  1736.72  1752.98  1715.47  1724.22  0.0066  0.0219   65301888   \n",
       "6924  1686.72  1710.47  1675.46  1697.97 -0.0152  0.0203   66173879   \n",
       "\n",
       "              金额    换手%   成交次数   target  \n",
       "6920  1472909400  0.608  67052  1689.22  \n",
       "6921  1467535000  0.586  60751  1712.97  \n",
       "6922  1408651100  0.554  59522  1724.22  \n",
       "6923   861618660  0.337  38464  1697.97  \n",
       "6924   852956240  0.341  37324  1680.46  "
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读取数据，0对应第一支股票，1对应第二只，以此类推\n",
    "df1=pd.read_excel('data.xlsx',0) \n",
    "df1=df1.iloc[3600:-10,1:]#选取从第3600行开始的数据 大概是2006年一月\n",
    "df1.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3325, 10)\n",
      "(3325,)\n"
     ]
    }
   ],
   "source": [
    "#进行数据归一化\n",
    "from sklearn import preprocessing\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "df0=min_max_scaler.fit_transform(df1)\n",
    "df = pd.DataFrame(df0, columns=df1.columns)\n",
    "X=df.iloc[:,:-1]\n",
    "y=df['target']#切片是前闭后开[)\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33150\n",
      "100\n",
      "3315\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "#构造训练集测试集 \n",
    "y=pd.DataFrame(y.values,columns=['goal'])\n",
    "x=X\n",
    "cut=10#取最后cut=10天为测试集\n",
    "X_train, X_test=x.iloc[:-cut],x.iloc[-cut:]#列表的切片操作，X.iloc[0:2400，0:7]即为1-2400行，1-7列\n",
    "y_train, y_test=y.iloc[:-cut],y.iloc[-cut:]\n",
    "X_train,X_test,y_train,y_test=X_train.values,X_test.values,y_train.values,y_test.values\n",
    "print(X_train.size)#通过输出训练集测试集的大小来判断数据格式正确。\n",
    "print(X_test.size)\n",
    "print(y_train.size)\n",
    "print(y_test.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\anoconda\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:6: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(16, kernel_initializer=\"uniform\", input_dim=10)`\n",
      "  \n",
      "E:\\anoconda\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:8: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(4, kernel_initializer=\"uniform\")`\n",
      "  \n",
      "E:\\anoconda\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:12: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  if sys.path[0] == '':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "3315/3315 [==============================] - 1s 292us/step - loss: 0.6150\n",
      "Epoch 2/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.5394\n",
      "Epoch 3/50\n",
      "3315/3315 [==============================] - 0s 7us/step - loss: 0.4586\n",
      "Epoch 4/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.3741\n",
      "Epoch 5/50\n",
      "3315/3315 [==============================] - 0s 7us/step - loss: 0.2888\n",
      "Epoch 6/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.2079\n",
      "Epoch 7/50\n",
      "3315/3315 [==============================] - 0s 7us/step - loss: 0.1376\n",
      "Epoch 8/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0832\n",
      "Epoch 9/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0477\n",
      "Epoch 10/50\n",
      "3315/3315 [==============================] - 0s 9us/step - loss: 0.0281\n",
      "Epoch 11/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0189\n",
      "Epoch 12/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0155\n",
      "Epoch 13/50\n",
      "3315/3315 [==============================] - 0s 7us/step - loss: 0.0141\n",
      "Epoch 14/50\n",
      "3315/3315 [==============================] - 0s 9us/step - loss: 0.0135\n",
      "Epoch 15/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0131\n",
      "Epoch 16/50\n",
      "3315/3315 [==============================] - 0s 7us/step - loss: 0.0127\n",
      "Epoch 17/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0123\n",
      "Epoch 18/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0119\n",
      "Epoch 19/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0115\n",
      "Epoch 20/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0111\n",
      "Epoch 21/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0107\n",
      "Epoch 22/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0103\n",
      "Epoch 23/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0099\n",
      "Epoch 24/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0096\n",
      "Epoch 25/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0092\n",
      "Epoch 26/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0088\n",
      "Epoch 27/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0084\n",
      "Epoch 28/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0081\n",
      "Epoch 29/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0077\n",
      "Epoch 30/50\n",
      "3315/3315 [==============================] - 0s 7us/step - loss: 0.0073\n",
      "Epoch 31/50\n",
      "3315/3315 [==============================] - 0s 9us/step - loss: 0.0070\n",
      "Epoch 32/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0067\n",
      "Epoch 33/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0063\n",
      "Epoch 34/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0060\n",
      "Epoch 35/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0057\n",
      "Epoch 36/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0054\n",
      "Epoch 37/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0051\n",
      "Epoch 38/50\n",
      "3315/3315 [==============================] - 0s 10us/step - loss: 0.0049\n",
      "Epoch 39/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0046\n",
      "Epoch 40/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0044\n",
      "Epoch 41/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0041\n",
      "Epoch 42/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0039\n",
      "Epoch 43/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0037\n",
      "Epoch 44/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0035\n",
      "Epoch 45/50\n",
      "3315/3315 [==============================] - 0s 7us/step - loss: 0.0033\n",
      "Epoch 46/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0032\n",
      "Epoch 47/50\n",
      "3315/3315 [==============================] - 0s 7us/step - loss: 0.0030\n",
      "Epoch 48/50\n",
      "3315/3315 [==============================] - 0s 8us/step - loss: 0.0029\n",
      "Epoch 49/50\n",
      "3315/3315 [==============================] - 0s 6us/step - loss: 0.0027\n",
      "Epoch 50/50\n",
      "3315/3315 [==============================] - 0s 7us/step - loss: 0.0026\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2c538c6ae10>"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#建立bp模型 训练 \n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Activation\n",
    "from keras.optimizers import Adam\n",
    "model = Sequential()  #层次模型\n",
    "model.add(Dense(16,input_dim=10,init='uniform')) #输入层，Dense表示BP层\n",
    "model.add(Activation('relu'))  #添加激活函数\n",
    "model.add(Dense(4,init='uniform')) #中间层\n",
    "model.add(Activation('sigmoid'))  #添加激活函数\n",
    "model.add(Dense(1))  #输出层\n",
    "model.compile(loss='mean_squared_error', optimizer='Adam') #编译模型\n",
    "model.fit(X_train, y_train, nb_epoch = 50, batch_size = 256) #训练模型nb_epoch=50次"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_73 (Dense)             (None, 16)                176       \n",
      "_________________________________________________________________\n",
      "activation_46 (Activation)   (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_74 (Dense)             (None, 4)                 68        \n",
      "_________________________________________________________________\n",
      "activation_47 (Activation)   (None, 4)                 0         \n",
      "_________________________________________________________________\n",
      "dense_75 (Dense)             (None, 1)                 5         \n",
      "=================================================================\n",
      "Total params: 249\n",
      "Trainable params: 249\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()#模型描述"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [],
   "source": [
    "#在训练集上的拟合结果\n",
    "y_train_predict=model.predict(X_train)\n",
    "y_train_predict=y_train_predict[:,0]\n",
    "y_train=y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Train Data')"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw=pd.concat([pd.DataFrame(y_train),pd.DataFrame(y_train_predict)],axis=1)\n",
    "draw.iloc[100:400,0].plot(figsize=(12,6))\n",
    "draw.iloc[100:400,1].plot(figsize=(12,6))\n",
    "plt.legend(('real', 'predict'),fontsize='15')\n",
    "plt.title(\"Train Data\",fontsize='30') #添加标题\n",
    "#展示在训练集上的表现 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [],
   "source": [
    "#在测试集上的预测\n",
    "y_test_predict=model.predict(X_test)\n",
    "y_test_predict=y_test_predict[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Test Data')"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw=pd.concat([pd.DataFrame(y_test),pd.DataFrame(y_test_predict)],axis=1);\n",
    "draw.iloc[:,0].plot(figsize=(12,6))\n",
    "draw.iloc[:,1].plot(figsize=(12,6))\n",
    "plt.legend(('real', 'predict'),loc='upper right',fontsize='15')\n",
    "plt.title(\"Test Data\",fontsize='30') #添加标题\n",
    "#展示在测试集上的表现 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集上的MAE/MSE/MAPE\n",
      "0.035685688649875265\n",
      "0.0025387128709298125\n",
      "86.26804878134983\n",
      "测试集上的MAE/MSE/MAPE\n",
      "0.0587253881853987\n",
      "0.004199689727701262\n",
      "8.840902502742047\n",
      "预测涨跌正确: 0.6666666666666666\n"
     ]
    }
   ],
   "source": [
    "#输出结果\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import math\n",
    "def mape(y_true, y_pred):\n",
    "    return np.mean(np.abs((y_pred - y_true) / y_true)) * 100\n",
    "print('训练集上的MAE/MSE/MAPE')\n",
    "print(mean_absolute_error(y_train_predict, y_train))\n",
    "print(mean_squared_error(y_train_predict, y_train) )\n",
    "print(mape(y_train_predict, y_train) )\n",
    "print('测试集上的MAE/MSE/MAPE')\n",
    "print(mean_absolute_error(y_test_predict, y_test))\n",
    "print(mean_squared_error(y_test_predict, y_test) )\n",
    "print(mape(y_test_predict, y_test) )\n",
    "y_var_test=y_test[1:]-y_test[:len(y_test)-1]\n",
    "y_var_predict=y_test_predict[1:]-y_test_predict[:len(y_test_predict)-1]\n",
    "txt=np.zeros(len(y_var_test))\n",
    "for i in range(len(y_var_test-1)):\n",
    "    txt[i]=np.sign(y_var_test[i])==np.sign(y_var_predict[i])\n",
    "result=sum(txt)/len(txt)\n",
    "print('预测涨跌正确:',result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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